Improving chemical reaction yield prediction using pre-trained graph neural networks
Abstract Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity. A promising solution to alleviate this issu...
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| Auteurs principaux: | , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
BMC
2024-03-01
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| Collection: | Journal of Cheminformatics |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1186/s13321-024-00818-z |
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